Research and improvement on packet capture mechanism in linux for high-speed network

With the increasing enlargement of network scale and the rapid development of network techniques, large numbers of the network applications begin to appear. Packet capture plays an important role as one basic technique used in each field of the network applications. In a high-speed network, the heavy traffic of network transmission challenges the packet capture techniques. This paper does an in-depth analysis on the traditional packet capture mechanisms in Linux, and then measures the performance bottleneck in the process of packet capture. The methods for improving the packet capture performance are presented and an optimized packet capture scheme is also designed and implemented. The test demonstrates that the new packet capture mechanism (Libpacket) can greatly improve the packet capture performance of the network application systems in a high-speed network.

A Scalable Intrusion Detection System for IPv6

The next generation protocol IPv6 brings the new challenges to the information security. This paper presents the design and implementation of a network based intrusion detection system that support both IPv6 protocol and IPv4 protocol. This system＇s architecture is focused on performance, simplicity, and scalability. There are four primary subsystems that make it up： the packet capture, the packet decoder, the detection engine, and the logging and alerting subsystem. This paper further describes a new approach to packet capture whose goal is to improve the performance of the capture process at high speeds. The evaluation shows that the system has a good performance to detect IPv6 attacks and IPv4 attacks, and achieves 61% correct detection rate with 20% false detection rate at the speed of 100 Mb·s^-1.

Feature extraction for sound source material recognition with impact sounds

Sound source recognition is a part of environmental sound recognition,which is one of the most important research areas in pattern recognition.Impact sounds carry much physical information associated with the sound sources,which makes impact sound based sound source recognition an important approach to improve recognition performance.In this study,the impact sound continuum synthesized with a ball-plate collision model is used for material recognition of the impacted plates.The basis function learning method and time-frequency representation methods,including the short time Fourier transform and the wavelet packet transform,are applied into classification and the recognition results are compared.The result shows that the features obtained by using the basis function learning perform better for material classification of the impacted plates than that by using the short time Fourier transform and the wavelet packet transform.This demonstrates the high efficiency and superiority of this method in material recognition of sound sources.

Performance analysis of high rate packet capture on multiprocessor platform

With the continuous improvement of transmission rate,high speed network links require good performance of packet capture.The multiprocessor platform has strong computational capabilities,and brings new chance for high rate packet capture.In this paper,we analyze the performance of common packet capture approaches that are based on general-purpose multiprocessor platform.The analysis contains two aspects:one is the maximum packet capture rate and throughput on multiprocessor platform,the other is central processing unit(CPU)load under the maximum capture rate.By comparing and analyzing the experimental result,we give the maximum packet capture rate and throughput of different capture approaches.Furthermore,we analyze the CPU load which is produced by two capture processes run on the multiprocessor platform simultaneously and make a comparison with the single capture process.